How to leverage AI in your Product: practical examples & tactics to maximize Value
- Thibaut Bardout
- il y a 6 jours
- 9 min de lecture
Dernière mise à jour : il y a 19 heures
Over the last 2 years, AI has shifted from novelty add-on to core value driver inside digital products. It now powers how experiences adapt to users, how decisions are automated, how content is created, and how products optimize themselves in real time. Capabilities once limited to large tech companies are now accessible to any SaaS or app team.
AI in products is not about adding a chatbot. It is about delivering things that were previously impossible: dynamic personalization, adaptive pricing, predictive features, instant creation tools, and continuous learning from user behavior.
In this guide, we break down AI’s 4 core pillars in product:
Personalization and Journey Optimization,
Native Generation and Creation,
Insight and Data Structuring, and
Core Predictive Features
and explore each through real-world examples and practical implementation tips.
This guide is for founders, product leaders, designers, engineers, and anyone looking to apply AI directly inside their product to deliver real user value, not hype or vanity feature
Content:
Pillar 1: Personalization & Journey Optimization: when products adapt to users
One of the biggest shifts AI brings to product design is the ability to shape an experience around each individual user instead of forcing everyone through the same static flow. In the past, onboarding and journeys were linear: same lessons, same nudges, same content sequence, same pacing. Today, AI systems quietly observe behavior, detect patterns, and adapt the path in real time—just like a great personal coach would.
You already feel this in many of the apps you use every day. Spotify reshapes your music feed as your taste evolves, sometimes within a single listening session. TikTok infers micro-interests from tiny interaction signals (rewatches, pauses, skips) and instantly rebuilds your “For You” feed. Grammarly adjusts tone, structure, and suggestions based on your writing style, so the edits feel personal rather than generic. Even Google Maps predicts where you’re likely going next and adapts routes based on patterns it has learned about your routines.
These examples all point to the same idea: personalization isn’t a feature anymore. It’s the invisible infrastructure that makes a product feel intuitive, interfaces that adapt when you struggle, flows that shorten when you move fast, content that reorders itself when you seem tired, agents that intervene when you’re about to drop off. None of this is marketed as AI, but it’s exactly what makes the experience feel smooth and “made for me.”
Deep Example: Duolingo’s Adaptive Learning Engine
What makes Duolingo powerful is that personalization isn’t an add-on… it’s the core engine of the product.
Behind every lesson, ML models constantly evaluate your level, memory decay, likelihood of success, and even your risk of dropping off. Difficulty adjusts in real time. If the system predicts you’re about to forget a word, it slips in a review. If your pronunciation or grammar shows a recurring weakness, the next exercise shifts to reinforce it. The journey reshapes itself with every tap.
This is powered by a stack of deep-learning models (RNNs/Transformers), item-response theory, sequence modeling, and predictive spaced-repetition algorithms. Together, they infer what you’ve mastered, what’s fragile, and the optimal moment to re-expose you to each concept.
The outcome: higher retention, smoother progression, fewer drop-offs, and stronger learning results—because the challenge continuously adjusts to keep you motivated without overwhelming you.
What’s especially clever is how invisible all this feels. There’s no “AI button.” The system adapts silently for 100M+ learners across wildly different languages, while handling real challenges like model drift, motivational differences, and fairness across courses.
Duolingo doesn’t use AI as a feature.
It uses AI to shape the journey itself—and that’s the essence of Pillar 1.
Pillar 2: Native Generation and Creation: when AI becomes the producer
AI is no longer limited to assisting the experience. In many products it has become the engine that produces the output itself. This is where generative models move from being a nice-to-have feature to a core value proposition: the user gives intent, and the product returns a finished asset.
Runway creates scenes and video shots that once required cameras and crews. Adobe expands or reimagines imagery with natural language. Notion drafts documents and summaries in seconds. Figma explores UI variations and layouts from simple descriptions.
These tools collapse the distance between intent and creation, letting professionals and non-professionals produce at a speed that used to be impossible.
From a product perspective, this pillar is about enabling users to skip the “blank page” and jump straight into creation. Instead of demanding skill in design, editing, writing, or production, the system absorbs the complexity and delivers a high-quality starting point or even a finished result. The user becomes the director, not the technician.
Deep Example: Canva’s Magic Design and Magic Studio
What makes Canva stand out is how quickly it turns intent into finished design. A user gives a sentence, an image, or a rough idea, and the system produces full assets like presentations, social posts, product visuals, or short videos. No design skill is required, because layout, color, and composition are handled automatically.
Behind the scenes, Canva blends diffusion models, multimodal embeddings, layout generators, and brand-style vectors.
The result is dramatic. Non-designers create polished work instantly, teams move faster, and creative output scales without more designers, driving engagement and premium use.
Canva also balances automation with control, offering strong first drafts that users can refine or regenerate. Canva does not add AI around the edges. It uses AI to produce the final asset itself, making creation almost instantaneous.
Pillar 3: Insight & Data Structuring: turning chaos into clarity
One of the most overlooked superpowers of modern AI is its ability to convert unstructured mess into structured, usable intelligence. Most work happens in chaos: long calls, sprawling email threads, noisy chats, scattered documents, vague meeting notes, endless tickets. Teams drown in information but starve for clarity.
AI flips this. It quietly listens, parses, extracts, clusters, summarizes, and organizes. A 60-minute meeting becomes a 5-point decision log. A messy inbox becomes a task list. Thousands of customer reviews collapse into a handful of themes. Support conversations turn into trend dashboards. Slack channels become knowledge pages. CRMs fill themselves.
You already see traces of this everywhere: Otter turning meetings into action items, Notion transforming notes into structured docs and databases, Intercom detecting ticket themes, HubSpot auto-tagging deal intent, Slack summarizing channels into digestible recaps. Products start to feel like they think with you, not merely store information for you.
Deep Example: Gong’s conversational Intelligence engine
Gong is a platform that records, transcribes, and analyzes sales conversations to extract actionable insights (e.g. objections, risks, and next steps) so teams can understand deal reality and improve performance.
What makes Gong remarkable is how it treats sales conversations not as ephemeral chatter but as structured data. Data is analyzed with AI models that detect objections, competitor mentions, risks, next steps, buyer sentiment, and even talk-ratio patterns.
Instead of relying on reps’ manual notes or subjective memory, Gong builds a reality layer of customer truth. The system surfaces deal risks, highlights coaching moments, and reveals patterns across thousands of interactions.
Technically, this relies on speech-to-text modeling, topic classification, sentiment analysis, and pattern detection across large conversational datasets. The models understand context, what signals a risk, what tone correlates with momentum, what phrasing hints at hesitation.
The impact for teams is huge.
They get clearer coaching, shorter sales cycles, and no more lost insights. Managers see what actually happened in calls. Reps stop taking notes and start listening. Pipelines become evidence-based instead of optimistic.
Part of Gong’s cleverness comes from its domain specialization. Its models are trained on millions of B2B calls, which gives it accuracy general-purpose transcription tools can’t match. It also faces hard challenges: privacy compliance, contextual ambiguity, and handling jargon that shifts across industries.
Pillar 4: Prediction & Revenue Optimization: when products make smarter decisions than teams
AI’s 4th major shift is its ability to predict outcomes and optimize decisions automatically. These systems don’t just react to user behavior… they anticipate it. They estimate churn risk, forecast demand, score leads, adjust prices, detect fraud, or decide which path a user should take next. What used to rely on fixed rules, intuition, or weekly reviews is now recalculated continuously.
You already see this in many products…
Airbnb dynamically prices nights based on demand curves; Stripe predicts fraud before a transaction settles; Shopify forecasts inventory needs; Uber balances rider and driver economics.
Even in more niche contexts, systems predict the best sequence of onboarding screens, when a user is likely to convert, or which discount will maximize margin instead of eroding it.
Across industries, the story is the same: AI takes decisions that were slow, subjective, or reactive and turns them into continuous, data-driven optimization.
The product becomes a strategist.
Deep Example: Poke’s adaptive pricing & negotiation onboarding
Poke is an AI-powered executive assistant that manages your schedule, communications, tasks, and daily workflows with near-human autonomy.
I came across Poke only recently, and the thing that struck me was the onboarding, through a whatsapp conversation.
It doesn’t begin with a form or an app login. It starts with them understanding who you are before you ever speak to anyone. They scan your public footprint, emails you’ve shared access to, LinkedIn history, X activity, past roles, lifestyle patterns, and build a prediction of 2 core variables:
How much value their assistant would realistically deliver for someone like you
Your likely ability and willingness to pay
That model shapes everything that follows.
And then comes the wild part: the negotiation.
The onboarding happens over WhatsApp, but it’s not a passive chat. You’re interacting with a system that already has a hypothesis about your profile and value.
If you claim you’re unemployed, the agent pushes back: “Interesting, but how did you manage that family trip 3 months ago?”
If you say you don’t need an assistant, they counter with specific, tailored use cases: your role, your habits, the tasks you postpone, the time you lose each week, the leverage they can unlock.
The pricing shifts dynamically based on predicted ROI for your situation. Wildely, it can be as wide as $3,000/month to (almost) free.
The arguments adapt based on your objections.
The narrative adjusts to your behavior.
It’s an unusually tight combination of:
value-based dynamic pricing
willingness-to-pay prediction
objection-handling models
LLM-driven negotiation flows
I’ve seen pricing engines before (I’ve built some myself) but I’ve never seen one woven so deeply into an onboarding conversation. The product effectively sells you on itself using your own data.
It’s bold, slightly unsettling, and extremely effective.
Leverage AI to drives real Outcomes
1. Start from Value
Begin by identifying the real friction in your product: slow decisions, wasted effort, inconsistent outcomes, or moments where users lose momentum.
AI should unlock a result the user cares about. Not add novelty for the sake of novelty.
If you can’t articulate the before → after improvement in one sentence, you’re not ready to build.
2. Ship Tiny, behavior-changing loops
Great AI products don’t arrive as massive launches.
They start with small, measurable loops that change a behavior: higher activation, fewer drop-offs, better decisions, faster execution.
Your job is not to show intelligence, it’s to prove impact fast.
3. Keep AI invisible
The best AI doesn’t announce itself.
It slots into the workflow, removes effort, and feels like the product “just works better.”
Avoid AI gimmicks: buttons that say “magic,” features that feel bolted on, or generative widgets users don’t actually need.
4. Use a simple Value × Feasibility × Differentiation filter
Before building anything, score it on 3 axes:
Value : does this meaningfully improve the user’s outcome?
Feasibility : do we have the data, systems, and precision to make this work?
Differentiation : does this create a moat or is it easily replicated?
You want a major bet… not sth someone can copy in weeks (or days) ?
High-value + high-feasibility + high-differentiation → ship it.
Everything else → not now.
5. Know when NOT to Use AI (with a real example)
AI isn’t a universal upgrade, and in some cases, it quietly makes products worse.
Here are the clearest signals you shouldn’t use it:
When simple rules work. If a deterministic rule solves the problem reliably, AI adds noise, not value.
When data is thin or messy. Weak signals produce confident mistakes.
When trust, clarity, or accountability matter. If users need a clear “why,” a black-box prediction is a liability.
When speed and transparency beat accuracy. A heuristic is often “good enough,” faster, and easier to trust.
When the core value is human or experiential. AI optimizes what’s measurable, not what actually creates loyalty.
A concrete example:
I recently came across a video of an AI system used in a café to track staff…
On paper, it looks brilliant… you can use the data to reduce bottlenecks, smoothen queues, and mainly increase staff efficiency.
But watching it, something felt off.
Because when I go to a café, I don’t return because the queue is 12% faster.
I return for the warmth of the place, the tiny rituals, the familiar exchange with the barista I know.
That quiet, human texture is the value, and none of it appears in a dashboard.
Systems like this accidentally teach teams to prioritise what’s measurable over what matters. Suddenly, the goal shifts from creating a great experience to moving dots on a heatmap.
And that’s where AI can genuinely harm a product: when it pushes you to optimise the visible metrics and forget the invisible value.
Summary
AI is reshaping how products are built, how they learn, and how they create value. But the teams who win won’t be the ones who ship the most AI, they’ll be the ones who use it with intention.
If you stay anchored in user value, start small, keep intelligence invisible, and understand when not to automate, AI becomes a force multiplier: it removes friction, adapts to each person, and helps users reach their goals with less effort and more clarity. And it shifts your focus from outputs (features, prompts, models) to true outcomes for the user.
The companies that treat AI as infrastructure, and measure success by the real outcomes users experience, not the amount of AI they ship, will define the next decade of product.
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